With the widespread usage of social media platforms, such as Facebook™, LinkedIn™, Twitter™, and/or Instagram™, millions of registered users are able to connect with each other and express their emotions about various events occurring around them. The events may be real-world events (e.g., earthquakes, floods, elections, and/or the like) or personal life events (e.g., wedding, graduation, employment, and/or the like). For all such events, the registered users may post, share, like, or dislike one or more messages, images, or videos on the social media platforms to express their emotions.
Typically, the real-world events are easy to detect due to the availability of substantial data. However, the personal life events are comparatively difficult to detect due to the limited availability of data. It is also difficult to identify whether the data is about the personal life event of a user, a life event related to a friend of the user or a general event directly or indirectly associated with the user. Nevertheless, the detection of the personal life events of users is equally important, as such detection may be utilized for providing useful recommendations to the users. Therefore, there is a need for a method and a system are needed to efficiently process the user data for detection of personal life events of the users.
Further limitations and disadvantages of the conventional and traditional approaches will become apparent to one skilled in the art, through a comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.